komamripy 0.0.2

Pulseq-compatible, high-performance MRI simulation in Python, powered by KomaMRI.jl.

komamripy

Pulseq-compatible, high-performance MRI simulation in Python.

komamripy is a thin Python interface to KomaMRI.jl, a high-performance Julia framework for MRI simulation. It exposes KomaMRI to Python through juliacall, so Python users can run fast CPU/GPU MRI simulations without writing any Julia.

Installation

Install by using uv (recommended) or pip (remove uv):

uv pip install git+https://github.com/JuliaHealth/komamripy

You do not need to install Julia yourself: juliacall provisions a suitable Julia automatically, and KomaMRI is installed on first import.

Quick start

The first import downloads Julia and precompiles KomaMRI, which can take a few minutes. Subsequent runs are fast.

import komamripy as km
import numpy as np

sys = km.Scanner()                       # scanner hardware
obj = km.brain_phantom2D()               # 2D brain phantom
seq = km.PulseDesigner.EPI_example()     # example EPI sequence

sim_params = km.KomaMRICore.default_sim_params()
sim_params["return_type"] = "mat"        # return the raw signal matrix

raw = km.simulate(obj, seq, sys, sim_params=sim_params)
signal = np.asarray(raw)

print(signal.shape)

A runnable version lives in examples/.

How it works

komamripy mirrors the KomaMRI Julia namespace: any function or type that KomaMRI exposes is available as an attribute of komamripy. Julia code therefore translates to Python almost line for line.

Simulation results are returned as Julia objects; convert array-like results to NumPy with numpy.asarray.

Status

komamripy is under active development as a Google Summer of Code project.

Currently supported: the core simulation pipeline and reading Pulseq files.

Planned: PyPI releases, GPU backend selection, tighter pypulseq integration, and differentiable workflows.